{"title":"The implantable loop recorder in high-risk syncope","authors":"","doi":"10.31887/hm.2020.82/vmehta","DOIUrl":"https://doi.org/10.31887/hm.2020.82/vmehta","url":null,"abstract":": Syncope is a common presentation, with the main challenge being identification of higher-risk patients.\u0000Often, treatment decisions are complex and require close monitoring prior to making a definitive management\u0000plan. This is particularly the case in those presenting with ventricular arrhythmia (VA). We describe a case of a\u000064-year-old male who presents with presyncope and probable VA. The patient subsequently had an implantable loop recorder (ILR) placed, which guided the final decision to insert an implantable cardioverter defibrillator.\u0000This case illustrates the general approach to patients presenting with syncope and VA and the use of newer\u0000technology, such as the ILR, in guiding definitive management strategies.","PeriodicalId":35477,"journal":{"name":"Heart and Metabolism","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73714929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Social media influencers in cardiology/metabolism—a force for good or evil?","authors":"","doi":"10.31887/hm.2020.82/dfrancis","DOIUrl":"https://doi.org/10.31887/hm.2020.82/dfrancis","url":null,"abstract":"Social media use within medicine has undeniably risen within the last decade, paralleling a trend in wider\u0000society. Twitter, with its threads and quiz elements, has become a prominent and accessible route of information\u0000distribution and debate in medicine. But is it a force for good or for harm?","PeriodicalId":35477,"journal":{"name":"Heart and Metabolism","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90463368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Which app is right for my CVD patient?","authors":"I. Gabizon, V. Bhagirath, C. Lokker","doi":"10.31887/hm.2020.82/igabizon","DOIUrl":"https://doi.org/10.31887/hm.2020.82/igabizon","url":null,"abstract":"Cardiovascular disease is a leading cause of mortality and morbidity, and despite efforts to identify and\u0000control cardiovascular risk factors, significant disease burden remains. As traditional strategies to reduce cardiovascular risk are challenged by lack of resources and growing populations, new strategies are deployed, including\u0000the use of smartphone applications (apps) designed to help patients manage their risk factors. For cardiovascular\u0000disease, some apps specifically address one risk factor, but others include a more holistic approach to manage\u0000multiple risk factors at once for primary and secondary prevention, whereas others serve as virtual cardiac rehabilitation intervention support. App stores show thousands of options in each app category, making it difficult to\u0000select the appropriate ones to recommend to patients. Very few apps in the app stores are rigorously validated for\u0000clinical efficacy or safety, making selection even more challenging. To address this, health organizations worldwide\u0000have created platforms to examine and appraise mobile health apps using standardized criteria to support clinician\u0000and patient app selection decisions. Now, with the rise of the COVID-19 pandemic, prolonged lockdowns have\u0000challenged traditional models of care. Telemedicine for cardiovascular disease patients is advancing virtual cardiac\u0000rehabilitation models to replace or improve traditional care.","PeriodicalId":35477,"journal":{"name":"Heart and Metabolism","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72880653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Wearable devices in cardiology","authors":"","doi":"10.31887/hm.2020.82/mcowie","DOIUrl":"https://doi.org/10.31887/hm.2020.82/mcowie","url":null,"abstract":"Wearables—sensors that are externally applied to the body to measure a signal and transmit or record\u0000the data for further analysis—are an industry worth billions of dollars annually. It is technically feasible to measure\u0000activity, blood pressure, and pulse, and to detect arrhythmia and potential heart failure decompensation\u0000via wearables. Relatively few studies have assessed the clinical value of wearables, and many remain curiosities\u0000or consumer “toys.” However, through attention to demonstrating accuracy and added value, it is possible\u0000for some technologies to be incorporated into diagnostic and treatment decision-making. Barriers to such\u0000transition include patient and physician acceptability, difficulties in incorporating the data into electronic medical\u0000records, and lack of reimbursement or regulatory approval. Cardiologists are becoming increasingly familiar with\u0000this developing field, but pressure for implementation may come more from the consumer than from the health\u0000care system.","PeriodicalId":35477,"journal":{"name":"Heart and Metabolism","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72461191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Big health data: the new frontier for cardiovascular disease","authors":"","doi":"10.31887/hm.2020.82/ggalasso","DOIUrl":"https://doi.org/10.31887/hm.2020.82/ggalasso","url":null,"abstract":"Despite improvement in diagnosis and management, cardiovascular disease (CVD) is the leading cause\u0000of death and hospitalization throughout the world. The expansion of digital cardiology presents outstanding\u0000opportunities for clinicians, researchers, and health care administrators to improve outcomes and sustainability\u0000of health systems. Electronic big health data combining electronic health records (EHRs) from diverse individuals\u0000across a wide variety of platforms may provide a real-time solution to questions and problems relating to health.\u0000Very large population studies based on EHR are efficient and cost-effective, and offer an alternative to traditional\u0000research approaches. Indeed, digital cardiology can help researchers to enhance, diagnose, and manage CVD\u0000using dedicated algorithms that allow targeted and personalized CVD treatments","PeriodicalId":35477,"journal":{"name":"Heart and Metabolism","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72673767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Digital Cardiology","authors":"","doi":"10.31887/hm.2020.82","DOIUrl":"https://doi.org/10.31887/hm.2020.82","url":null,"abstract":"","PeriodicalId":35477,"journal":{"name":"Heart and Metabolism","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88991181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Big data, AI, and mHealth–the digital evolution of cardiology","authors":"","doi":"10.31887/hm.2020.82/mmarber","DOIUrl":"https://doi.org/10.31887/hm.2020.82/mmarber","url":null,"abstract":"","PeriodicalId":35477,"journal":{"name":"Heart and Metabolism","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88995234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Artificial intelligence in cardiology","authors":"","doi":"10.31887/hm.2020.82/ltarassenko","DOIUrl":"https://doi.org/10.31887/hm.2020.82/ltarassenko","url":null,"abstract":"In the first wave of artificial intelligence (AI), rule-based expert systems were developed, with modest\u0000success, to help generalists who lacked expertise in a specific domain. The second wave of AI, originally\u0000called artificial neural networks but now described as machine learning, began to have an impact with multilayer\u0000networks in the 1980s. Deep learning, which enables automated feature discovery, has enjoyed spectacular\u0000success in several medical disciplines, including cardiology, from automated image analysis to the identification\u0000of the electrocardiographic signature of atrial fibrillation during sinus rhythm. Machine learning is now embedded\u0000within the NHS Long-Term Plan in England, but its widespread adoption may be limited by the “black-box”\u0000nature of deep neural networks.","PeriodicalId":35477,"journal":{"name":"Heart and Metabolism","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82489474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Perturbations in myocardial energy metabolism in the ischemic heart","authors":"","doi":"10.31887/hm.2020.81/jussher","DOIUrl":"https://doi.org/10.31887/hm.2020.81/jussher","url":null,"abstract":"As an organ that must continuously pump oxygenated blood throughout the body, the heart has an\u0000enormous metabolic demand, which is primarily met via oxidative metabolism of fatty acids and carbohydrates.\u0000Because of its high metabolic demand, during times of reduced oxygen supply such as ischemia, the heart\u0000becomes highly susceptible to injury, and if flow is not re-established, myocardial tissue is lost and can result\u0000in death (myocardial infarction). Of interest, both myocardial ischemia and reperfusion are associated with a\u0000number of perturbations in energy metabolism that contribute to the pathology of ischemic heart disease. This\u0000includes marked elevations in glycolysis to counteract the reduction in oxidative metabolism, whereas fatty acids\u0000predominate as the primary fuel source for residual oxidative metabolism. During the early stages of cardiac\u0000recovery after successful reperfusion of the ischemic heart, fatty acid oxidation rates also rapidly recover at\u0000the expense of low glucose oxidation rates. These metabolic perturbations increase myocardial acidosis due\u0000to glycolysis being uncoupled from glucose oxidation, which impairs cardiac efficiency. As such, therapeutic\u0000approaches to stimulate glucose oxidation or inhibit fatty acid oxidation have the potential to correct dysregulated myocardial energy metabolism during ischemia and reperfusion, which improves cardiac efficiency and\u0000may lead to improved clinical outcomes in people with ischemic heart disease. L","PeriodicalId":35477,"journal":{"name":"Heart and Metabolism","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87044061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Troponin elevation: is it ischemia?","authors":"","doi":"10.31887/hm.2020.81/eorsini","DOIUrl":"https://doi.org/10.31887/hm.2020.81/eorsini","url":null,"abstract":"Cardiac troponin is the preferred biomarker for myocardial infarction, thanks to its sensitivity and absolute\u0000specificity for the heart. The availability of high-sensitivity assays (hs-cTnT and hs-cTnI), capable of measuring with\u0000excellent analytical precision very low levels of circulating troponin, raised the issue of whether transient ischemia is\u0000a sufficient stimulus for troponin release. For this purpose, in a series of patients submitted to a stress test (exercise\u0000ECG/echo test; dipyridamole echo test; dobutamine echo test), we measured plasma levels of hs-cTnT at baseline\u0000and 6 hours after the end of the test. Plasma concentrations of hs-cTnT significantly increased in the vast majority of\u0000patients after the test. Significant elevations were documented in response to each stressor, regardless of the test\u0000result, after both positive and negative tests. Moreover, troponin significantly increased in response to the stress,\u0000both in patients with and in patients without obstructive coronary artery disease. Despite a good sensitivity (80%\u0000and 89%), troponin showed a very low specificity (32% and 47%) for stress-induced ischemia and coronary artery\u0000disease, respectively. Myocardial release of troponin is a multifactorial process, mediated not only by cardiomyocyte\u0000necrosis, but also through several different mechanisms such as myocardial ischemia, increase in cardiac work,\u0000and hemodynamic overload. Transient elevation of high sensitivity cardiac troponin is not a useful tool for detecting\u0000spontaneous or stress-induced ischemia. L","PeriodicalId":35477,"journal":{"name":"Heart and Metabolism","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90214296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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